Process Excursion Detection

ABSTRACT

A method for analyzing defect information on a substrate, including logically dividing the substrate into zones, and detecting defects on the substrate to produce the defect information. The defect information from the substrate is analyzed on a zone by zone basis to produce defect level classifications for the defects within each zone. The zonal defect level classifications are analyzed according to at least one analysis method. The defect level classifications are preferably selected from a group of defect level classifications that is specified by a recipe. Preferably, the at least one analysis method includes at least one of zonal defect distribution, automatic defect classification, spatial signature analysis, and excursion detection. The defect level classifications preferably include at least one of individual defect, defect cluster, and spatial signature analysis signature. In one embodiment the defect information is logically divided into configurable zones after the defects on the substrate have been detected.

FIELD

This invention relates to the field of inspection equipment. Moreparticularly, this invention relates to inspection equipment for theintegrated circuit fabrication industry.

BACKGROUND

Modern integrated circuits, such as monolithic semiconductor devicesformed on substrates of Group IV materials such as silicon or germanium,or Group III-V materials such as gallium arsenide, or combinations ofsuch materials, are fabricated using extremely complex processes. Theseprocesses can be generally categorized into a few different groups, suchas photolithographic, deposition, and etching. Process steps that fallinto one or more of these different groups are applied over and overagain, forming the integrated circuit layer by layer, until it iscompleted.

Because both the integrated circuit itself and the process by which itis formed are so complex, there are innumerable ways in which defectsand flaws can creep in to the fabrication process. Such defects are ableto not only degrade the ability of the integrated circuit to functionproperly, but can reduce its anticipated lifetime, or cause it to notfunction at all. These defects can be related to a myriad of differentsources, such as materials issues, handling issues, and processcapability issues.

Because of the great number of potential pitfalls during integratedcircuit fabrication, and the extreme cost associated with the defectscaused by such, it is very important to become aware of defects andidentify their sources as soon as possible. In this manner, there mightbe some type of remedy or rework that can be timely applied to theintegrated circuits that exhibit the defects, or more likely, the sourceof those defects can be corrected as soon as possible, so thatadditional integrated circuits are not impacted by the problem.

Thus, in-line inspections are an important part of the integratedcircuit fabrication process. These inspections are conducted at manydifferent points during the fabrication process, and in some instancesare conducted virtually after each individual process step. In thismanner, defects and their sources are hopefully detected and identifiedin a timely manner, before too many integrated circuits are affected.

One important classification of such inspections are opticalinspections, meaning inspections that are intended to identify defectsthat can be seen in some manner. These optical inspections havetraditionally been done manually, meaning that a human inspector looksat the substrate, typically called a wafer, on which the integratedcircuits are formed. First, an inspection may be conducted with thenaked eye, which hopefully detects large defects, or large patterns ofdefects. Next, the inspector may look at the substrate under some typeof microscope to determine additional information about the nature ofthe defects, or to detect defects which cannot be observed by the nakedeye.

Unfortunately, such manual inspection of substrates is somewhatinsufficient. For example, such manual inspection is extremely tediousto perform. Thus, human inspectors tend to tire and stop noticing themore subtle defects. In addition, due to the difference in the training,experience, and ability from one inspector to the next, the data that isproduced in this manner tends to be extremely difficult to integrateinto a production system that can use the data to identify problems andimprove processes.

For this reason, various automated optical inspection methods andanalysis systems have been developed. Unfortunately, such systems tendto be very limited in their capabilities as compared to a humaninspector, generally because of their more limited cognitive andassociative abilities as compared to a human. Thus, such automatedoptical inspection and analysis systems often miss things that anexperienced and careful human inspector would find.

Thus, well trained and alert human inspectors tend to recognize andidentify defects better, but automated systems are less subjective andmore repeatable. What is needed, therefore, are automated analysismethods that increase the ability of an automated inspection andanalysis system to recognize the sources of defects.

SUMMARY

The above and other needs are met by a method for analyzing defectinformation on a substrate, including logically dividing the substrateinto zones, and detecting defects on the substrate to produce the defectinformation. The defect information from the substrate is analyzed on azone by zone basis to produce defect level classifications for thedefects within each zone. The zonal defect level classifications areanalyzed according to at least one analysis method. The defect levelclassifications are preferably selected from a group of defect levelclassifications that is specified by a recipe. Preferably, the at leastone analysis method includes at least one of zonal defect distribution,automatic defect classification, spatial signature analysis, andexcursion detection. The defect level classifications preferably includeat least one of individual defect, defect cluster, and spatial signatureanalysis signature. In one embodiment the defect information islogically divided into configurable zones after the defects on thesubstrate have been detected.

According to another aspect of the invention there is described a methodfor detecting process excursions from defect information from asubstrate, including analyzing the defect information based on a list ofselectable factors to determine spatial signature analysis signatures. Aprocess problem identification is selectively assigned to the substratebased on a combination of more than one spatial signature analysissignatures detected on the substrate. The list of selectable factorspreferably includes at least one of a number of events, an averagedensity of an event, a number of die affected, an effective length ofevent, an area covered by event, and a location of event relative tosubstrate center.

Preferably, the step of selectively assigning the process problemidentification is accomplished with a table that includes a numericprocess problem identification, a string process problem identification,a Boolean expression of component spatial signature analysis signatures,a remedial action, a notification action, a layer identification, and aseverity level. The step of selectively assigning a process problemidentification to the substrate most preferably includes assigning asubstrate identification to the substrate and storing the substrateidentification and the process problem identification in a database.Preferably, more than one process problem identification can be assignedto each substrate.

According to still another aspect of the invention there is described amethod for detecting process excursions, including detecting defects ona selectable set of substrates. The defects detected on the set ofsubstrates are composited into an effectual substrate defect set. Theeffectual substrate defect set is analyzed with a spatial analysisroutine. The spatial analysis routine preferably includes at least oneof spatial signature analysis and repeater analysis. In variousembodiments, the selectable set of substrates includes substrates thatall belong to a given lot, or every nth substrate from a given data set,where n is an integer that is greater than one. The step of compositingthe defects into an effectual substrate defect set preferably includesat least one of translating and rotating a data set from a givensubstrate as needed to align with data sets from other substrates.

According to yet another aspect of the invention there is described amethod for classifying defects on a substrate, including analyzing thedefects with a first analysis routine that is adapted to classify largerpatterns of defects, and analyzing the defects that were not classifiedwith the first analysis routine with a second analysis routine that isadapted to classify smaller defects, using output from the firstanalysis routine as input to the second analysis routine. Preferably,the first analysis routine is spatial signature analysis, the secondanalysis routine is automated defect classification, and the output fromthe first analysis routine includes bounding boxes from the spatialsignature analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

Further advantages of the invention are apparent by reference to thedetailed description when considered in conjunction with the figures,which are not to scale so as to more clearly show the details, whereinlike reference numbers indicate like elements throughout the severalviews, and wherein:

FIG. 1 is a flow chart of a method of zonal analysis according to thepresent invention.

FIG. 2 is a flow chart of a method of determining process excursionsbased on combinations of spatial signature analysis signatures accordingto the present invention.

FIG. 3 is a flow chart of a method of spatial wafer stacking accordingto the present invention.

FIG. 4 is a flow chart of a method of hierarchical classification ofdefects according to the present invention.

FIG. 5 is a flow chart of a method of using spatial signature analysisin zonal analysis according to the present invention.

DETAILED DESCRIPTION

This disclosure presents four methods that can be used individually orin combination with one another to enhance the identification of defectsand their sources during automated inspection and analysis processes.These various methods can be implemented in a variety of ways. Forexample, these methods can be implemented in stand-alone opticalinspection tools, or as modules that are called by an analysis routine,whether that routine be based in an adaptable, programmable inspectiontool or remotely from the inspection tool, or as stand alone routinesthat may be invoked as desired on a data set that is input or otherwiseidentified by the user.

Most preferably, these methods are implemented as software routines thatrun on a computing platform, such as a standard personal computer typeplatform, as might be adapted to control the operation of and receivedata from an automated optical inspection tool. Thus, in variousembodiments, these methods take the form of either hardware or software,or a combination of the two. For example, embodiments of the presentmethods can be sold to end users as software routines on computerreadable media, such as diskettes or compact disks. Alternately,embodiments can take the form of firmware upgrades to be installed inexisting inspection tools. Thus, the descriptions of the variousembodiments herein, which are mostly described in terms of methods, arenot to be limited to method steps alone, but also to physicalembodiments of those methods.

These methods are primarily concerned with the analysis of data that hasalready been generated, such as data that is generated by the opticalinspection of a substrate, where the data represents the properties ofdefects that have been detected, such as location, shape, and size.However, in alternate embodiments, the data could conceivably begenerated in some other manner. Further, the primary purpose of themethods described below is to determine in a timely manner whether adeleterious and correctable event has occurred, such as a processexcursion, so that the event can be corrected before additionalsubstrates are misprocessed or otherwise damaged. However, in otherembodiments the data is analyzed for other purposes, such as for generalprocess improvement.

Zonal Defect Level Classification

Various problems that occur during substrate processing frequently leavecharacteristic signatures on the substrate, in the form of opticallydetectable defect distributions. For example, one type of signature is aring, which might be perceptible to the naked eye. However, thering—which is the signature—is actually formed not of a single,continuous defect, but of a series of smaller defects. Recognizing thesignature from an analysis of the pattern of the individual defects iscalled spatial signature analysis. If spatial signature analysis is notperformed, it is likely that one will miss the forest for the tress, soto speak. In many cases, such signature defect distributions aretraceable to a particular process problem, and also appear in specificand repeatable locations on the substrate from one occurrence toanother.

To perform zonal analysis, the defects on the substrate are detected, asgiven in block 12 of FIG. 1. Either the substrate itself or the defectdata is logically divided into several zones of interest as given inblock 14, and defects are binned according to the zones in which theyare found. One of the disadvantages of prior art methods of zonalanalysis is that they are extremely inflexible. They can only analyzesubstrate zones for certain event level properties, such as the numberof defects and the density of the defects in each zone. Thus, they areparticularly unsuitable for an integrated analysis that uses additionalanalysis techniques. For example, because these event level propertiesare divided into zones, they tend to be in a form that is difficult, ifnot impossible, to use in spatial signature analysis, or other advancedcognitive analyses.

According to the present invention, zonal analysis is recipe driven, inthat all aspects of the zonal analysis relevant to the user areconfigurable through a recipe. Preferably, the zonal analysis works onarbitrary defect distributions specified by a list of attributes. Forexample, the zonal analysis can be performed with individual defects,defect clusters, spatial signature analysis signatures, and so forth inmind.

Thus, rather than producing event level classifications like the priorart methods of zonal analysis, the present method generates defect levelclassifications as given in block 16, using the recipe instructionsinput by the user, or entered from a central database. Various classidentifications are preferably assigned to each of the defects in theinput distribution. For example, these class identifications canidentify classes such as clusters, spatial signature analysissignatures, and so forth.

One of the significant differences between the preferred methodaccording to the present invention and existing zonal analyses is thatthe latter are limited to identifying zonal membership for individualevents, such as defects, defective pixels, defective dies, and so forth.However, the method described herein preferably classifies individualevents into visually recognizable patterns of events, such as a spatialsignature, as a first step. Each recognized pattern, rather than justindividual events, is then subsequently treated as a single entityduring a zonal analysis. Users preferably configure a list of patternsthat are of interest to them, and also a list of zones that are ofinterest to them. Most preferably, the patterns in the one list areassociated with the zones in the other list, based on a relationshipbetween them as determined by previously identified process problems.

For example, users may wish to identify long straight scratches or othersuch events (the pattern of interest) near the wafer notch (the locationof interest), because they know that the relationship between thispattern and this location indicates an identifiable process problem,which is preferably automatically brought to the user's attention by thesystem when the relationship is detected in the data. The user maydecide to not set a specific relationship for long straight scratches inother locations on the substrate, preferring instead to just be madeaware of such, without being notified of any predetermined relationship.Alternately, the user may set up several such relationships, dependingat least in part upon where on the substrate the pattern of interest islocated.

Thus, zonal analysis is performed on patterns of interest treated asdetermined by the user recipe. If a signature falls into a zone ofinterest, the signature is preferably reclassified according to the userrecipe. The resulting classification provides much more accurateinformation for identification of certain types of process problems. Itallows the user to readily distinguish between two causes that leavespatially identical signatures on the substrate, but which are locatedat different characteristic zones on the substrate.

On the other hand, existing zonal analyses merely separate events intozones and determine certain properties of the zones, such as the numberand density of events in each zone. However, they do not tie togetherthis information with either the spatial pattern of event clusters orany other attribute of the event.

Thus, the preferred methods according to the present invention provide amore flexible analysis targeted at spatially correlated events ofinterest. The result of the analysis is a reclassification of the wholepattern into a new class that carries information not only about thespatial pattern but also about its spatial context on the substrate. Theexisting methods only determine membership of individual events tosubstrate zones and calculate simple properties of the zones based onthe result, such as the number of events per zone. However, calculationof these properties is easily available in the present method as well.

It is noted that the target for zonal analysis in the present inventiondoes not necessarily need to be a spatial pattern. It can be a clusterof events based on any other event level attribute, such as repeater orcluster identification. Further, the word “event” as used hereinincludes several concepts, including defect, defective pixels, defectivedie, or any other spatially definable event on the substrate.

FIG. 5 is a more detailed flow chart of the process flow 52 according tothe present invention. As mentioned above, the detected defects areanalyzed, and classifications are preferably generated for spatiallyrelated defects, as given in block 54. In addition, higher level objectsare preferably created, consisting of spatial signatures of particularshape. Each of the signatures generated in that step are then preferablytreated as a single entity for the sake of zonal analysis, as given inblock 56. It is noted that the preferred method also analyzes theindividual defects during zonal analysis, in addition to the signatures.If the signature of a particular type falls into a particular zone ofinterest on the substrate, as identified by the user such as through arecipe, then the process branches as given in block 58. When arelationship is found, the entire signature of defects is reclassifiedaccording to the user's recipe, as given in block 62. If a relationshipis not found, then the existing classification of the signature isretained.

Because this zonal analysis produces defect level classifications only,and not the event level classifications of prior art systems, thisanalysis is not limited to any specific intended application, such asthe mere binning of the number of defects and the density of defects.Instead, the present method allows any attribute based analysis tofollow, as given in block 18 of FIG. 1. For example, defect density ordefect count can be calculated for each zone, as was previouslypossible, however, a new classification can be used for excursiondetection, for example. Thus, prior art zonal analyses provide only asubset of the capabilities of the new method.

Multi-Factor Spatial Signature Analysis

Detection of problems such as process excursions is typically based oninformation such as total defect count of signatures, total number ofsignatures of a particular type (such as a scratch), and detection of aparticular type of signature (such as a ring). However, current methodsdo not identify process problems based on combinations of informationfrom different types of detected signatures, whereas the methoddescribed herein looks at combinations of signatures to identifyprocessing problems.

For example, detection of a single signature such as ring may indicateone process problem or excursion, whereas detection of the same ring incombination with heavy vertical lines may indicate a completelydifferent process excursion. The first condition may not be harmful,whereas the second condition may require immediate attention to reducefuture yield losses. Therefore, looking at combinations of detectedsignatures tends to provide information that is more valuable thanlooking at isolated signature data, and increases the ability of themethod to differentiate one process problem from another

For example, the signature information that can be used in this methodincludes the number of defects, the average density of the defects, thenumber of dice on the substrate effected by the defects, the effectivelength of the defects, the area over which the defects are scattered,and the location of the defects, such as with respect to the center ofthe substrate. By looking at combinations of such information, ratherthan at isolated events, a greater amount of process information can bededuced.

For example, in one embodiment, the defects on the substrate aredetected, as given in block 22 of FIG. 2. The various signaturesinherent in the defect data are then detected, as given in block 24.Predetermined combinations of signatures are next found, as given inblock 26, which predetermined combinations indicate various processproblems. Each process problem is assigned an identification, and thesignatures associated with the process problem are identified. When thatcombination of signatures is detected, an identification is assigned tothe inspected substrate that exhibits the detected condition, and thisinformation is stored in a database of some type, such as for futurereference, as given in block 28. Preferably, multiple identificationscan be assigned to each substrate, based on the signature combinationsthat are found on the substrate. For example, there may be more than oneprocess with a problem at a given time, the effects of which can beoptically identified on the substrate.

This method can be implemented such as in a table that is interrogatedduring an analysis, where each row includes information in regard to aparticular process problem. For example, the table may includeinformation such as a numeric and string identification for the problem,a Boolean or other expression of the signature combination thatidentifies that problem, a description of remedial or reporting actionsto be taken when the problem is identified, and an identification of therelative severity of the problem. In addition, the table may include anidentification of the layer on which the problem occurs, such as aplanarized layer, a lithographic layer, an etched layer, a depositedlayer, and so forth. This may be important, because a ring signature mayhave a different interpretation on a planarized layer than it does on alithographic layer, for example.

Spatial Substrate Stacking

Spatial analysis operates on information that is typically extractedfrom a single substrate that underwent a particular process or toolmanipulation. The success of the spatial analysis, such as signatureanalysis or repeater analysis, tends to rely on whether the singlesubstrate from which the data is collected is sufficiently impacted bythe process or tool excursion, such that the substrate receives asufficient number of spatial characteristics, and spatialcharacteristics of a sufficient intensity, such that the spatialcharacteristics rise above the noise level of other optically detectableevents on the substrate. Since these conditions are not always met,substrate level spatial analysis may leave a growing critical processexcursion undetected until it causes serious damage to the yield of theprocess or tool.

To detect these critical but hard to find excursions, spatial signatureanalysis is preferably performed on defect data that is accumulated frommany different substrates, as given in block 32 of FIG. 3, that all havethe same or similar process history. Preferably, a mechanism is used toselect for stacking the data from many different substrates with acommon processing history, as given in block 34. The data selectionprocess preferably includes correlating defect data with work inprogress information, and is preferably user configurable. The work inprogress data includes the common process history information for thesubstrates. Thus, the work in progress data includes information suchas, for example, which substrates were processed in a given chamber. Bystacking the substrate information in such a manner, process departuresfor a given process history can be more easily detected.

For example, the selection process may include a single substrate(meaning no stacking), an entire lot of substrates, an entire data setas determined by something other than lot, every n^(th) inspectedsubstrate in the data set, or some other sampling mechanism as desired,and a previously computed split attribute, such as by process tool orprocess chamber.

The spatial information contained within the different substrates thatare selected as given above is preferably combined for the analysis, asgiven in block 36. This process preferably includes creating aneffectual set of composite data that looks like it came from a singlesubstrate, so that it can be processed by any spatial analysis routine,such as spatial signature analysis or repeater analysis, as given inblock 38. The effectual substrate data is referred to herein as the“substrate stack.” Preferably, the method wherein the substrate stack iscreated takes into account various possible differences in the data setsfor individual substrates, such as data translation, rotation, andscaling misalignments that are a result of variations between differentpieces of inspection equipment, temperature variations, alignmentvariations, and so forth.

Spatial substrate stacking thus enhances the ability of the analysisroutine to provide an early detection of process excursions that do notleave a pronounced signature on a substrate, at least not initially.Substrate stacking can be integrated into various systems, such as aninspection tool analysis engine, where it is invoked as a part of aruntime inspection sequence. For example, the method can be used tostack a selected subset of substrate data at the end of each lot ofsubstrates that is inspected by the tool, and then used to report theresults through the tool connection to the management engineeringsystem, which is preferably configured to trigger on such an excursion.

Alternately, the method can be implemented in a load time analysisengine, and invoked from a lot based event. This allows stacking on datafrom many different inspection tools and from inspection tools made bydifferent manufacturers. Further yet, the method can be implemented inan engineering data analysis engine, which thereby allows the greatestflexibility in selecting data from a large defect and work in progressdatabase.

Hierarchical Defect Classification

Substrate defects range in size from big defects of a few centimeters insize, to small defects having a size on the order of the thickness of ahuman hair. For example, on a typical three hundred millimeter diametersubstrate, defects range from the global signature of a big circularring defect covering the substrate edge boundary, to a small particlethat may be only twenty-five microns in diameter. Hierarchicalclassification uses an ordered selection of analysis methods, such asspatial signature analysis and automatic defect classification, toclassify the defects, thereby providing the ability to automaticallyclassify substrate defects of various sizes in a semiconductor substrateprocess.

The hierarchical classification method described herein automaticallyclassifies substrate defects using a multi-stepped approach, such as atwo step approach, which is implemented after the defects are detected,as given in block 42 of FIG. 4. The first step is to automaticallyanalyze the defects using the first analysis method as given in block44, which is preferably spatial signature analysis, which is useful toclassify larger defects into a set of defined signatures, as given inblock 46. The second step is to use the detailed spatial signatureanalysis results including the bounding boxes, of those small localizeddefects that did not produce spatial signature analysis signatures, asgiven in block 48, to further analyze their gray level images andautomatically classify the small defects into a set of defined defectclasses, preferably using automated defect classification, as given inblock 50.

The preferred flow of the inspection post-processing is as follows. Theinspection tool preferably generates an image of the substrate. Thedefects on the substrate, represented by “defective” pixels in theimage, are identified by one or more of a number of differentthresholding methods, and the defects are converted into points on thesubstrate. Most preferably, this includes identification of a boundingbox size. Spatial signature analysis of the entire substrate, or atleast of the inspected area of the substrate, is then performed on thisdata, which is preferably the collection of points representingthresholded defective pixels.

The result of the spatial analysis is two-fold. First, spatiallyrecognizable patterns of defects are classified by the spatial signatureanalysis. Second, the rest of the defects are either random events orbelong to spatial clusters whose shape is not recognizable by thespatial signature analysis with sufficient accuracy or confidence. Inthe latter case, spatial signature analysis still provides usefulinformation by providing the bounding box around the clusters ofspatially correlated defects to indicate to the subsequent automaticdefect classification scheme what to classify, i.e. what are the patchimages. Finally, the automatic defect classification routine receives animage for each bounding box supplied by the spatial signature analysis,and performs detailed classification using the full image information.

This automatic hierarchical classification method enables the quickdetection of yield related process problems, and implementation ofcorrective action into the processes in a timely manner, which speeds uproot cause analysis in yield process management and reduces the cost ofsubstrate rework. The opportunities for savings are expected to begreater on shrinking technologies having higher substrate and die costs.

The foregoing description of preferred embodiments for this inventionhas been presented for purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed. Obvious modifications or variations are possible inlight of the above teachings. The embodiments are chosen and describedin an effort to provide the best illustrations of the principles of theinvention and its practical application, and to thereby enable one ofordinary skill in the art to utilize the invention in variousembodiments and with various modifications as are suited to theparticular use contemplated. All such modifications and variations arewithin the scope of the invention as determined by the appended claimswhen interpreted in accordance with the breadth to which they arefairly, legally, and equitably entitled.

1-15. (canceled)
 16. A method for classifying defects on a substrate,the method comprising the steps of: analyzing the defects with a firstanalysis routine that is adapted to classify larger patterns of defects,and analyzing the defects that were not classified with the firstanalysis routine with a second analysis routine that is adapted toclassify smaller defects, using output from the first analysis routineas input to the second analysis routine.
 17. The method of claim 16,wherein the first analysis routine is spatial signature analysis. 18.The method of claim 16, wherein the second analysis routine is automateddefect classification.
 19. The method of claim 16, wherein the outputfrom the first analysis routine includes bounding boxes from a spatialsignature analysis.
 20. The method of claim 16, wherein the firstanalysis routine is spatial signature analysis and the second analysisroutine is automated defect classification, and the output from thefirst analysis routine includes bounding boxes.